1 Run PCAnsgd for PWS populations

Using the ‘PWSonly’ sites (~770k snps) Running PCAngsd requires several steps

1.1 Analysis Steps

1.1.2 Using bcftools to subset the VCF file using prune.in file

1.1.3 Convert VCF to the beagle format and run PCAnsgd

#create vcf files with only prune.in sites

bcftools view -R Data/vcf/3pops_newMD2000_maf05_75_5_0.5_pruned.prune.in.sites.txt Data/vcf/3pops.MD2000_new.maf05.vcf.gz > Data/vcf/3pops_MD2000_maf05_pruned.vcf

bgzip /Data/vcf/3pops_MD2000_maf05_pruned.vcf

#Create beagle files (at farm: create_beagle.sh) - same scripts to obtain allele frequency can output beagle files as well (calulateAF_xx.sh)
#  angsd -GL -doGlf 2

angsd -out /home/ktist/ph/data/new_vcf/MD3000/3pops_MD2000_maf05 -fai /home/jamcgirr/ph/data/c_harengus/c.harengus.fa.fai -doGlf 2 -doMaf 3 -doMajorMinor 4 -doPost 1 -doGeno 2 -vcf-pl /home/ktist/ph/data/new_vcf/MD7000/3pops/3pops_PWS91_maf05.vcf.gz -ref /home/jamcgirr/ph/data/c_harengus/c.harengus.fa 

# Reformat the prune.in file

1.1.4 Craete a slurm script file to create beagle files

sink(paste0("../Data/Slurmscripts/create_beagle.sh"))
cat("#!/bin/bash -l\n")
cat(paste0("#SBATCH --job-name=beagle \n"))
cat(paste0("#SBATCH --mem=16G \n")) 
cat(paste0("#SBATCH --ntasks=8 \n"))
cat(paste0("#SBATCH --nodes=4 \n"))
cat(paste0("#SBATCH -e =beagle.err  \n"))
cat(paste0("#SBATCH --time=72:00:00  \n"))
cat(paste0("#SBATCH -p high  \n"))
cat("\n\n")
cat("module load vcftools")     
cat("\n\n")
    
for (i in 1:26){
    cat(paste0("vcftools --gzvcf /home/ktist/ph/data/new_vcf/MD3000/3pops_MD2000_maf05_pruned.vcf.gz  --chr chr",i))
    cat(paste0(" --out /home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_c",i," --BEAGLE-PL \n"))
}

cat("\n")

#remove the head line and cat beagle files
for (i in 2:26){
    cat(paste0("sed -e '1, 1d' < /home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_c",i,".BEAGLE.PL > /home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_c",i,".2.BEAGLE.PL \n"))
}
cat("\n")

cat("cat /home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_c1.BEAGLE.PL ") 
for (i in 2:26){
    cat(paste0("/home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_c",i,".2.BEAGLE.PL "))
}
cat(paste0(" > /home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_BEAGLE.PL \n"))
cat("gzip /home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_BEAGLE.PL \n\n")

sink(NULL)

1.1.5 Craete a slurm script file to run pcansgd

# Run PCAangsd for the entire chromosomes and each chromosome
sink(paste0("../Data/Slurmscripts/runPCAnsgd.sh"))
cat("#!/bin/bash -l\n")
cat(paste0("#SBATCH --job-name=PCAnsgd \n"))
cat(paste0("#SBATCH --mem=16G \n")) 
cat(paste0("#SBATCH --ntasks=8 \n"))
cat(paste0("#SBATCH --nodes=4 \n"))
cat(paste0("#SBATCH -e PCAnsgd.err  \n"))
cat(paste0("#SBATCH --time=72:00:00  \n"))
cat(paste0("#SBATCH -p high  \n"))
cat("\n\n")
cat("module load angsd \n")     
cat("module load deprecated/python \n")     
cat("module load deprecated/pcangsd")     
cat("\n\n")
    
cat("python /home/jamcgirr/apps/pcangsd/pcangsd.py -b /home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_BEAGLE.PL.gz -o /home/ktist/ph/data/angsd/PCAngsd/3pops_MD2000 -threads 16 \n")
sink(NULL)


# run each chromosome separately 
sink(paste0("../Data/Slurmscripts/runPCAnsgd_byChromosome.sh"))
cat("#!/bin/bash -l\n")
cat(paste0("#SBATCH --job-name=PCAnsgdC \n"))
cat(paste0("#SBATCH --mem=16G \n")) 
cat(paste0("#SBATCH --ntasks=8 \n"))
cat(paste0("#SBATCH --nodes=4 \n"))
cat(paste0("#SBATCH -e PCAnsgdbyChrom.err  \n"))
cat(paste0("#SBATCH --time=72:00:00  \n"))
cat(paste0("#SBATCH -p high  \n"))
cat("\n\n")
cat("module load angsd \n")     
cat("module load deprecated/python \n")     
cat("module load deprecated/pcangsd")     
cat("\n\n")

for(i in 1:26){
    cat(paste0("python /home/jamcgirr/apps/pcangsd/pcangsd.py -beagle /home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_c",i,".BEAGLE.PL.gz -o /home/ktist/ph/data/angsd/PCAngsd/3pops_MD2000_c",i," -threads 24 \n"))
}

sink(NULL)

2 Results of PCAngsd

2.1 All chromosomes

pop_info<-read.csv("../Data/Sample_metadata_892pops.csv")
pop_info<-pop_info[,c("Sample","Population.Year","pop","Year.Collected")]
colnames(pop_info)[4]<-"year"
pops<-unique(pop_info$Population.Year[grep("PWS|SS|TB",pop_info$Population.Year)])
pop3<-pop_info[pop_info$Population.Year %in% pops,]

pop3$year[pop3$year==2007|pop3$year==2006]<-"2006/2007"
pop3$year<-factor(pop3$year, levels=c(1991, 1996, "2006/2007", 2017))

C <- as.matrix(read.table(paste0("../Data/PCAangsd/3pops_MD2000.cov")))
e <- eigen(C)
pca <-data.frame(Sample=pop3$Sample, 
                 pop= pop3$pop,
                 year=pop3$year,
                 PC1=e$vectors[,1],PC2=e$vectors[,2],
                 PC3=e$vectors[,3],PC4=e$vectors[,4],
                 PC5=e$vectors[,5],PC6=e$vectors[,6],
                 PC7=e$vectors[,7],PC8=e$vectors[,8],
                 stringsAsFactors=FALSE)

prop_explained <- c()
for (s in e$values[1:10]) {
    #print(s / sum(e$values))
    prop_explained <- c(prop_explained,round(((s / sum(e$values))*100),2))
}
pca$pop<-factor(pca$pop, levels=c("PWS","SS","TB"))

ggplot()+
    geom_point(data = pca, aes(x = PC1, y = PC2, fill = pop, color = pop, shape=year), size = 1.8)+
    scale_fill_manual(values=paste0(cols[c(2,3,1)],"4D"), guide="none")+
    scale_color_manual(values=cols[c(2,3,1)], name="Population")+
    xlab(paste("PC 1: ", prop_explained[1],"%\n",sep = ""))+
    ylab(paste("PC 2: ", prop_explained[2],"%\n",sep = ""))+
    theme_bw()+
    scale_shape_manual(values=c(23,25,3,21), name="Year")
ggsave("../Output/PCA/3pop_allChromosomes.png", height = 3.8, width = 5.2, dpi=300)

2.2 Each chromosome separately

Plots<-list()
chr<-paste0("c", c(1:26))
for (i in 1:length(chr)){
    C <- as.matrix(read.table(paste0("../Data/PCAangsd/3pops_MD2000_",chr[i],".cov")))
    e <- eigen(C)
    pca <-data.frame(Sample=pop3$Sample, 
                     pop=pop3$pop,
                     year=pop3$year,
                     PC1=e$vectors[,1],PC2=e$vectors[,2],
                     PC3=e$vectors[,3],PC4=e$vectors[,4],
                     PC5=e$vectors[,5],PC6=e$vectors[,6],
                     PC7=e$vectors[,7],PC8=e$vectors[,8],
                     stringsAsFactors=FALSE)
    
    prop_explained <- c()
    for (s in e$values[1:10]) {
        prop_explained <- c(prop_explained,round(((s / sum(e$values))*100),2))
    }
    Plots[[i]]<-ggplot()+
                    geom_point(data = pca, aes(x = PC1, y = PC2, fill = pop, color = pop, shape=year), size = 2.5)+
                    scale_fill_manual(values=paste0(cols[c(2,3,1)],"4D"), guide="none")+
                    scale_color_manual(values=cols[c(2,3,1)], name="Population")+
                    xlab(paste("PC 1: ", prop_explained[1],"%\n",sep = ""))+
                    ylab(paste("PC 2: ", prop_explained[2],"%\n",sep = ""))+
                    theme_bw()+
                    scale_shape_manual(values=c(23,25,3,21), name="Year")+
                    ggtitle(paste0("Chr",i))+theme(legend.position = "none")
}
g <- arrangeGrob(do.call(grid.arrange, c(Plots, ncol=5)))
ggsave(g, file="../Output/PCA/3pops_MD2000_PCA_byChromosome.png",width = 35, height = 30)

{pdf("../Output/PCA/3pops_MD2000_PCA_byChromosome.pdf",width = 30, height = 30)
do.call(grid.arrange, c(Plots, ncol=5))
dev.off()
}

# Plot legends
   p<-ggplot()+
        geom_point(data = pca, aes(x = PC1, y = PC2, fill = pop, color = pop, shape=year), size = 2.5)+
        scale_fill_manual(values=paste0(cols[c(2,3,1)],"4D"), guide="none")+
        scale_color_manual(values=cols[c(2,3,1)], name="Population")+
        xlab(paste("PC 1: ", prop_explained[1],"%\n",sep = ""))+
        ylab(paste("PC 2: ", prop_explained[2],"%\n",sep = ""))+
        theme_bw()+
        scale_shape_manual(values=c(23,25,3,21), name="Year")+
        ggtitle(paste0("Chr",i))

as_ggplot(get_legend(p))
ggsave("../Output/PCA/3pops_MD2000_PCA_byChromosome_legend.png",width = 1.5, height = 5)

2.3 Color by Year

ycols<-c("#f2f0f7","#cbc9e2","#9e9ac8","#6a51a3")

Plots<-list()
chr<-paste0("c", c(1:26))
for (i in 1:length(chr)){
    C <- as.matrix(read.table(paste0("../Data/PCAangsd/3pops_MD2000_",chr[i],".cov")))
    e <- eigen(C)
    pca <-data.frame(Sample=pop3$Sample, 
                     pop=pop3$pop,
                     year=pop3$year,
                     PC1=e$vectors[,1],PC2=e$vectors[,2],
                     PC3=e$vectors[,3],PC4=e$vectors[,4],
                     PC5=e$vectors[,5],PC6=e$vectors[,6],
                     PC7=e$vectors[,7],PC8=e$vectors[,8],
                     stringsAsFactors=FALSE)
    
    prop_explained <- c()
    for (s in e$values[1:10]) {
        prop_explained <- c(prop_explained,round(((s / sum(e$values))*100),2))
    }
    
    Plots[[i]]<-ggplot()+
                    geom_point(data = pca, aes(x = PC1, y = PC2, fill = year, color = year, shape=pop), size = 2.5)+
                    scale_fill_manual(values=paste0(ycols,"4D"), guide="none")+
                    scale_color_manual(values=ycols, name="Year")+
                    xlab(paste("PC 1: ", prop_explained[1],"%\n",sep = ""))+
                    ylab(paste("PC 2: ", prop_explained[2],"%\n",sep = ""))+
                    theme_bw()+
                    scale_shape_manual(values=c(16,17,15), name="Population")+
                    ggtitle(paste0("Chr",i))+theme(legend.position = "none")
}

{pdf("../Output/PCA/3pops_MD2000_PCA_byChromosome_byYear_nolegends.pdf",width = 30, height = 30)
do.call(grid.arrange, c(Plots, ncol=5))
dev.off()
}

g <- arrangeGrob(do.call(grid.arrange, c(Plots, ncol=4)))
ggsave(g, file="../Output/PCA/3pops_MD2000_PCA_byChromosome_byYear.png",width = 35, height = 40)


# Save the legend
    p<-ggplot()+
            geom_point(data = pca, aes(x = PC1, y = PC2, fill = year, color = year, shape=pop), size = 2.5)+
            scale_fill_manual(values=paste0(ycols,"4D"), guide="none")+
            scale_color_manual(values=ycols, name="Year")+
            xlab(paste("PC 1: ", prop_explained[1],"%\n",sep = ""))+
            ylab(paste("PC 2: ", prop_explained[2],"%\n",sep = ""))+
            theme_bw()+
            scale_shape_manual(values=c(16,17,15), name="Population")+
            ggtitle(paste0("Chr",i))
as_ggplot(get_legend(p))
ggsave("../Output/PCA/3pops_MD2000_PCA_byChromosome_byYear_legend.png",width = 1.5, height = 5)




3 PCAnsgd Selection scan

3.1 File preps and running PCAngsd

#Divide the BEAGLE.PL file into each population

# BEAGLE.PL.gz files available upon request
bea<-fread("../Data/vcf/MD2000/3pops_MD2000_pruned_BEAGLE.PL.gz") 
pop_info<-read.csv("../Data/Sample_metadata_892pops.csv")
pop_info<-pop_info[,c("Sample","Population.Year","pop","Year.Collected")]
colnames(pop_info)[4]<-"year"
pops<-unique(pop_info$Population.Year[grep("PWS|SS|TB",pop_info$Population.Year)])


# for each populations
pops3<-c("PWS","TB","SS")
for (i in 1:length(pops3)){
    colums<-grep(pops3[i], colnames(bea))
    vec<-c(1:3, colums)
    df<-bea[,..vec]
    write.table(df, file=gzfile(paste0("../Data/vcf/MD2000/", pops3[i],"_MD2000_pruned.BEAGLE.PL.gz")), sep="\t", quote = F, row.names = FALSE )
}



y1<-c("PWS07","PWS17","PWS91","PWS96")
comb1<-t(combn(y1, 2))
y2<-c("TB06","TB17","TB91","TB96")
comb2<-t(combn(y2, 2))
y3<-c("SS06","SS17","SS96")
comb3<-t(combn(y3, 2))
comb4<-data.frame(V1=c("PWS07","PWS17","PWS91","PWS96","PWS07","PWS17","PWS96","SS06","SS17","SS96"), V2=c("TB06","TB17","TB91","TB96","SS06","SS17","SS96","TB06","TB17","TB96"))
comb<-rbind(comb1,comb2,comb3, comb4)

for (i in 3:nrow(comb)){
    pop1<-comb[i,1]
    pop2<-comb[i,2]
    col1<-grep(pop1, colnames(bea))
    col2<-grep(pop2, colnames(bea))
    vec<-c(1:3, col1, col2)
    df<-bea[,..vec]
    write.table(df, file=gzfile(paste0("../Data/vcf/MD2000/", pop1,"_",pop2,"_MD2000_pruned.BEAGLE.PL.gz")), sep="\t", quote = F, row.names = FALSE )
}


# Create slurm scripts
bfiles<-list.files("../Data/new_vcf/MD2000/", pattern="BEAGLE.PL.gz")

sink("../Data/Slurmscripts/pcansgd_selection_md2000.sh")
cat("#!/bin/bash -l")
cat("\n")
cat(paste0("#SBATCH --job-name=selection \n"))
cat(paste0("#SBATCH --mem=24G \n")) 
cat(paste0("#SBATCH --ntasks=8 \n")) 
cat(paste0("#SBATCH --nodes=4  \n")) 
cat(paste0("#SBATCH -e selection.err  \n"))
cat(paste0("#SBATCH --time=144:00:00  \n"))
#cat(paste0("#SBATCH --mail-user=ktist@ucdavis.edu ##email you when job starts,ends,etc \n"))
#cat(paste0("#SBATCH --mail-type=ALL \n"))
cat(paste0("#SBATCH -p high  \n"))
cat("\n")
cat("module load angsd
module load deprecated/python
module load deprecated/pcangsd")
cat("\n\n")

for (i in 1:length(bfiles)){
  fname<-gsub("_MD2000_pruned.BEAGLE.PL.gz",'', bfiles[i])
  cat(paste0("python /home/jamcgirr/apps/pcangsd/pcangsd.py -beagle /home/ktist/ph/data/new_vcf/MD3000/beagle/",bfiles[i]," -o /home/ktist/ph/data/angsd/selection/",fname,"_selection -selection -sites_save \n"))
}

sink(NULL)

#(ran pcansgd_selection_md2000.sh, pcansgd_selection_md2000_1.sh, pcansgd_selection_md2000_2.sh at Farm on 6/28/23)

3.2 Plot the results of each population

# Selection scan results are available at: OSF Storage: https://osf.io/wrca4 Data/PCAngsd/selection/

pop_info<-read.csv("../Data/Sample_metadata_892pops.csv")
pop_info<-pop_info[,c("Sample","pop","Year.Collected")]
colnames(pop_info)[3]<-"year"


#######
### Selection ###
library(RcppCNPy) # Numpy library for R

## function for QQplot
qqchi<-function(x,...){
    lambda<-round(median(x)/qchisq(0.5,1),2)
    qqplot(qchisq((1:length(x)-0.5)/(length(x)),1),x,ylab="Observed",xlab="Expected",...);abline(0,1,col=2,lwd=2)
    legend("topleft",paste("lambda=",lambda))
}

### read in seleciton statistics (chi2 distributed)
# Each column reflect the selection statistics along a tested PC (they are χ²-distributed with 1 degree of freedom.)
s<-npyLoad("../Data/PCAangsd/selection/PWS_selection.selection.npy")

## make QQ plot to QC the test statistics
qqchi(s)
ncol(s)

## read positions 
p<-read.table("../Data/PCAangsd/selection/PWS_selection.sites",colC=c("factor","integer"),sep=":")
names(p)<-c("chr","pos")


# 1. 1 axis:
#convert test statistic to p-value
p$pval<-1-pchisq(s,1)
p$loc<-1:nrow(p)
p$pval.log<--log10(p$pval)

## make manhatten plot
plot(-log10(p$pval),col=p$chr,xlab="Chromosomes",main="Manhattan plot", pch=".")

# 2. if more than 1 axis (ncol(s)>1)
# p$pval1<-pval[,1]
# p$pval2<-pval[,2]
# p$loc<-1:nrow(p)
# p$pval1.log<--log10(p$pval1)
# p$pval2.log<--log10(p$pval2)

## make Manhattan plots
pops1<-c("PWS","SS","TB")
evens<-paste0("chr",seq(2,26, by=2))

for (i in 1:length(pops1)){
    s<-npyLoad(paste0("../Data/PCAangsd/selection/",pops1[i],"_selection.selection.npy"))
    p<-read.table(paste0("../Data/PCAangsd/selection/",pops1[i],"_selection.sites"),colC=c("factor","integer"),sep=":")
    names(p)<-c("chr","pos")

    n<-ncol(s)
    if (n==1){
        p$pval<-1-pchisq(s,1)
        p$loc<-1:nrow(p)
        p$pval.log<--log10(p$pval)
    }
    #count the number of sites per chromosomes
    poss<-data.frame(chr=paste0("chr",1:26))
    k=1
    for (j in 1:26){
        df<-p[p$chr==paste0("chr",j),]
        poss$start[j]<-k
        poss$end[j]<-k+nrow(df)-1
        k=k+nrow(df)
    }
    poss$x<-poss$start+(poss$end-poss$start)/2

    p$color<-"steelblue"
    p$color[p$chr %in% evens]<-"lightblue"
    ggplot(data=p, aes(x=loc, y=pval.log, color=color))+
        geom_point(size=0.1)+
        scale_color_manual(values=c("lightblue","steelblue"), guide='none')+
        scale_x_continuous(name="Chromosome position", breaks=poss$x, labels=1:26)+
        theme_classic()+ylab("-log10(p-value)")+
        ggtitle(pops1[i])
    ggsave(paste0("../Output/PCA/selection_MD2000/",pops1[i],"_selection_scan.png"), width=8, height=4,dpi=300)
           
}

# -log10(0.05) = 1.30103
# -log10(0.05/232644) #6.667722
###  Significant p-value for 232,644 loci is 6.67  ###

  • NO sites above 6.7

3.3 Plot results of pariwise comparison

y1<-c("PWS07","PWS17","PWS91","PWS96")
comb1<-t(combn(y1, 2))
y2<-c("TB06","TB17","TB91","TB96")
comb2<-t(combn(y2, 2))
y3<-c("SS06","SS17","SS96")
comb3<-t(combn(y3, 2))
comb<-rbind(comb1, comb2, comb3)

evens<-paste0("chr",seq(2,26, by=2))

for (i in 1:nrow(comb)){
    pop1<-comb[i,1]
    pop2<-comb[i,2]
    s<-npyLoad(paste0("../Data/PCAangsd/selection/",pop1, "_",pop2,"_selection.selection.npy"))
    p<-read.table(paste0("../Data/PCAangsd/selection/",pop1, "_",pop2,"_selection.sites"),colC=c("factor","integer"),sep=":")
    names(p)<-c("chr","pos")

    n<-ncol(s)
    if (n==1){
        p$pval<-1-pchisq(s,1)
        p$loc<-1:nrow(p)
        p$pval.log<--log10(p$pval)
    }
    if (n>1){
        p$pval<-pval[,1]
        p$loc<-1:nrow(p)
        p$pval.log<--log10(p$pval)
    }
    #count the number of sites per chromosomes
    poss<-data.frame(chr=paste0("chr",1:26))
    k=1
    for (j in 1:26){
        df<-p[p$chr==paste0("chr",j),]
        poss$start[j]<-k
        poss$end[j]<-k+nrow(df)-1
        k=k+nrow(df)
    }
    poss$x<-poss$start+(poss$end-poss$start)/2

    p$color<-"steelblue"
    p$color[p$chr %in% evens]<-"lightblue"
    ggplot(data=p, aes(x=loc, y=pval.log, color=color))+
        geom_point(size=0.1)+
        scale_color_manual(values=c("lightblue","steelblue"), guide='none')+
        scale_x_continuous(name="Chromosome position", breaks=poss$x, labels=1:26)+
        theme_classic()+ylab("-log10(p-value)")+
        ggtitle(paste0(pop1, " - ", pop2))
    ggsave(paste0("../Output/PCA/selection_MD2000/",pop1, "_",pop2,"_selection_scan.png"), width=8, height=4,dpi=300)
}




4 NGSadmix

4.1 Run NGSadmix (at Farm or locally)

  • NGSadmix_md2000_1.sh,NGSadmix_md2000_2.sh,NGSadmix_md2000_3.sh

4.2 Run CLUMPAL to find best K

  • (The best K =3)

4.2.1 Compile all likelihood numbers from log files into 1 (logfile)

#linux code (won't work with unix)
(for log in `ls *.log`; do grep -oP '3pops_pruned_maf05_\K[^ ]+|like=\K[^ ]+' $log; done) > 3pops_logfile_k3
(for log in `ls *.log`; do grep -oP '3pps_pruned_maf05_\K[^ ]+|like=\K[^ ]+' $log; done) > 3pops_logfile_k2
(for log in `ls *.log`; do grep -oP '3pops_pruned_maf05_\K[^ ]+|like=\K[^ ]+' $log; done) > 3pops_logfile_k4

4.3 Read ‘logfile’ & create an input file for Clumpak

log2<-read.table("../Data/ngsadmix/3pops_logfile_k2", sep="\t", header =FALSE)
log3<-read.table("../Data/ngsadmix/3pops_logfile_k3", sep="\t", header =FALSE)
log4<-read.table("../Data/ngsadmix/3pops_logfile_k4", sep="\t", header =FALSE)
log4<-data.frame(log4[c(FALSE,TRUE),])

logs<-data.frame(K=c(rep(2, times=10),rep(3, times=10),rep(4, times=10)), Liklihood=c(log2$V1,log3$V1, log4))
write.table(logs, "../Output/ngsadmix/3popslogs.txt", sep="\t", row.names = F, col.names = F, quote=F)
# DO NOT use special character in the file name
# Must have at least three K values

# upload the logs.txt to Clumpak website
# http://clumpak.tau.ac.il

#'Estimating the Best K (from Clumpak)'
#Wed Jul  5 20:15:52 2023: Ln'(3) = 277336.622395895
#Wed Jul  5 20:15:52 2023: Ln'(4) = 264322.030625194
#Wed Jul  5 20:15:52 2023: |Ln''(K=3)| = 13014.5917707011
#Wed Jul  5 20:15:52 2023: Delta(K=3) = 130.085671688741
#Wed Jul  5 20:15:52 2023: Max Delta K: 130.085671688741
#Wed Jul  5 20:15:52 2023: Optimal K by Evanno is: 3
#Wed Jul  5 20:15:56 2023: Using median values of Ln Prob of Data to calculate Prob(K=k):
#Wed Jul  5 20:15:56 2023: Prob(K=2) = 0
#Wed Jul  5 20:15:56 2023: Prob(K=3) = 0
#Wed Jul  5 20:15:56 2023: Prob(K=4) = 1
#Wed Jul  5 20:15:56 2023: Max Probability: 1
#Wed Jul  5 20:15:56 2023: The k for which Prob(K=k) obtains the highest value is: 4
#
#
#

  • 2 makes more sense than 3 but the reuslts chose “K=3” as the best

4.4 Run evalAdmix

#Create bash scripts to run locally
sink("../Data/Slurmscripts/evalAdmix_runlocal.sh")
cat("#!/bin/bash\n\n")

for (i in 2:3){
    cat("evalAdmix -beagle Data/new_vcf/MD2000/beagle/3pops_MD2000_pruned_BEAGLE.PL.gz ")
    cat(paste0("-fname Data/ngsadmix/3pops_pruned_maf05_k",i,"_run1.fopt.gz "))
    cat(paste0("-qname Data/ngsadmix/3pops_pruned_maf05_k",i,"_run1.qopt "))
    cat("-P 10 ")
    cat(paste0("-o Data/ngsadmix/evaladmix/output.corres.k",i,".txt\n"))
}
sink(NULL)

5 Plot results

source("visFuns.R")

#Output files
qfiles<-list.files("../Data/ngsadmix/",pattern="_run1.qopt")
ofiles<-list.files("../Data/ngsadmix/evaladmix/",pattern="output.corres")

#population info
pop<-read.csv("../Data/Sample_metadata_892pops.csv")
pop<-pop[grep("PWS|SS|TB", pop$pop),]
pop$Population.Year<-factor(pop$Population.Year, levels=c("TB91","TB96","TB06","TB17","PWS91","PWS96","PWS07","PWS17","SS96","SS06","SS17"))
poporder<-paste(pop$Population.Year[order(pop$Population.Year)])
pop_order<-c("TB91","TB96","TB06","TB17","PWS91","PWS96","PWS07","PWS17","SS96","SS06","SS17")

for (i in 1:length(qfiles)){
    # extract K from the file name
    if (i!=3) {
        oname<-ofiles[i]
        k<-gsub("[^0-9.]", "", ofiles[i])
        k<-as.integer(gsub("\\.",'',k))
    }
    if (i==3) k=3
    
    #read the qopt file for k=k
    q<-read.table(paste0("../Data/ngsadmix/", qfiles[i]))
    
    #order according to population and plot the NGSadmix results
    q$id<-pop$Population.Year
    q<-q[order(q$id),]
    
    ord<-orderInds(pop = as.vector(poporder), q = q[,1:(i+1)])
    
    xlabels<-data.frame(x=tapply(1:length(poporder),list(poporder), mean))
    xlabels$pop<-factor(rownames(xlabels), levels=pop_order)
    xlabels<-xlabels[order(xlabels$pop),]
    
    #color assignment
    if (i==1) colors=cols[c(1,2)]
    if (i==2) colors=cols[c(1,6,2) ]
    if (i==3) colors=cols[c(1,6,2,3) ]

    {png(paste0("../Output/ngsadmix/3pops_Admix_plot_k",k,".png"), height = 3.5, width=8, unit="in", res=300)
    barplot(t(q[,1:(i+2)])[,ord],col=colors,space=0,border=NA,xaxt="n",xlab="",ylab=paste0("Admixture proportions for K=",k))
    text(xlabels$x,-0.05,xlabels$pop,xpd=T, srt=90, adj=1,cex=0.8)
    abline(v=cumsum(sapply(unique(poporder[ord]),function(x){sum(pop[ord,"Population.Year"]==x)})),col=1,lwd=1.2)
    dev.off()}
    
    #Plot the correlation matrix from evalAdmix
    if (i!=3) {
        r<-read.table(paste0("../Data/ngsadmix/evaladmix/",ofiles[i]))
    
    # Plot correlation of residuals
    {pdf(paste0("../Output/ngsadmix/3pops_evalAdmix_corplot_k",k,".pdf"), height = 8, width=10)
    plotCorRes(cor_mat = r, pop = as.vector(pop[,"Population.Year"]), ord = ord, title=paste0("Evaluation of admixture proportions with K=",k), max_z=0.1, min_z=-0.1)
    dev.off()}
    }
}

# plot all 10 replicates for k=3
qfiles<-list.files("../Data/ngsadmix/",pattern="^3pops_pruned_maf05_k3+.*.qopt")
for (i in 2:10){
    q<-read.table(paste0("../Data/ngsadmix/", qfiles[i]))
    
    #order according to population and plot the NGSadmix results
    q$id<-pop$Population.Year
    q<-q[order(q$id),]
    
    ord<-orderInds(pop = as.vector(poporder), q = q[,1:3])
    
    xlabels<-data.frame(x=tapply(1:length(poporder),list(poporder), mean))
    xlabels$pop<-factor(rownames(xlabels), levels=pop_order)
    xlabels<-xlabels[order(xlabels$pop),]
    
    #color assignment
    colors=cols[c(1,6,2)]
    
    {png(paste0("../Output/ngsadmix/3pops_Admix_k3_rep",i,".png"), height = 3.5, width=8, unit="in", res=300)
    barplot(t(q[,1:3])[,ord],col=colors,space=0,border=NA,xaxt="n",xlab="",ylab=paste0("Admixture proportions for K=",k), main=paste0("rep",i))
    text(xlabels$x,-0.05,xlabels$pop,xpd=T, srt=90, adj=1,cex=0.8)
    abline(v=cumsum(sapply(unique(poporder[ord]),function(x){sum(pop[ord,"Population.Year"]==x)})),col=1,lwd=1.2)
    dev.off()}
}
---
title: "PCAngsd/NGSadmix"
output": html_notebook
output:
  html_notebook:
      toc: true 
      toc_float: true
      number_sections: true
      theme: lumen
      highlight: tango
      code_folding: hide
      df_print: paged
---
# Run PCAnsgd for PWS populations 
Using the 'PWSonly' sites (~770k snps) 
Running PCAngsd requires several steps
```{r eval=FALSE, message=FALSE, warning=FALSE, include=FALSE}
source("BaseScripts.R")
library(dplyr)
library(cowplot)
library(knitr)
library(kableExtra)
library(data.table)
library(ggpubr)
gcols<-c('#ffffb3','#8dd3c7','#bebada')
```

## Analysis Steps
### Prune SNPs: Using Plink to prune highly linked snps  
* Need to reformat the output (xxx.prune.in) file to subset a vcf file (rather than using it for  pruing a ped/bed file) 
```{bash eval=FALSE, echo=TRUE}
# vcf files not in the repository are available at: OSF Storage: https://osf.io/wrca4 Data/vcf/


#first create ped/bed files with adding variant id
#add variant id & create ped/bed files
plink --vcf Data/vcf/3pops.MD2000_new.maf05.vcf.gz --set-missing-var-ids @:#[ph]\\$r,\\$a --make-bed --out Data/new_vcf/MD3000/3pops_newMD2000_maf05

plink --bfile Data/vcf/3pops_newMD2000_maf05 --recode --tab --out Data/vcf/3pops_newMD2000_maf05

#find highly correlated sites for pruning
plink --file Data/vcf/3pops_newMD2000_maf05 --indep-pairwise 75'kb' 5 0.5 --out Data/new_vcf/MD3000/3pops_newMD2000_maf05_75_5_0.5_pruned

```


* Reformat the prune.in file
```{r eval=FALSE, message=FALSE, warning=FALSE}
#Reformat prun.in file with running the reformat_prunin.R
source("reformat_prunin.R")
reformat_prunin("../Data/vcf/3pops_newMD2000_maf05_75_5_0.5_pruned.prune.in")

#Output in ../Data/vcf/3pops_newMD2000_maf05_75_5_0.5_pruned.prune.in.sites.txt
```

### Using bcftools to subset the VCF file using prune.in file

### Convert VCF to the beagle format and run PCAnsgd

```{bash eval=FALSE, echo=TRUE}
#create vcf files with only prune.in sites

bcftools view -R Data/vcf/3pops_newMD2000_maf05_75_5_0.5_pruned.prune.in.sites.txt Data/vcf/3pops.MD2000_new.maf05.vcf.gz > Data/vcf/3pops_MD2000_maf05_pruned.vcf

bgzip /Data/vcf/3pops_MD2000_maf05_pruned.vcf

#Create beagle files (at farm: create_beagle.sh) - same scripts to obtain allele frequency can output beagle files as well (calulateAF_xx.sh)
#  angsd -GL -doGlf 2

angsd -out /home/ktist/ph/data/new_vcf/MD3000/3pops_MD2000_maf05 -fai /home/jamcgirr/ph/data/c_harengus/c.harengus.fa.fai -doGlf 2 -doMaf 3 -doMajorMinor 4 -doPost 1 -doGeno 2 -vcf-pl /home/ktist/ph/data/new_vcf/MD7000/3pops/3pops_PWS91_maf05.vcf.gz -ref /home/jamcgirr/ph/data/c_harengus/c.harengus.fa 

# Reformat the prune.in file
```

### Craete a slurm script file to create beagle files  
```{r eval=FALSE, message=FALSE, warning=FALSE}

sink(paste0("../Data/Slurmscripts/create_beagle.sh"))
cat("#!/bin/bash -l\n")
cat(paste0("#SBATCH --job-name=beagle \n"))
cat(paste0("#SBATCH --mem=16G \n")) 
cat(paste0("#SBATCH --ntasks=8 \n"))
cat(paste0("#SBATCH --nodes=4 \n"))
cat(paste0("#SBATCH -e =beagle.err  \n"))
cat(paste0("#SBATCH --time=72:00:00  \n"))
cat(paste0("#SBATCH -p high  \n"))
cat("\n\n")
cat("module load vcftools")     
cat("\n\n")
    
for (i in 1:26){
    cat(paste0("vcftools --gzvcf /home/ktist/ph/data/new_vcf/MD3000/3pops_MD2000_maf05_pruned.vcf.gz  --chr chr",i))
    cat(paste0(" --out /home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_c",i," --BEAGLE-PL \n"))
}

cat("\n")

#remove the head line and cat beagle files
for (i in 2:26){
    cat(paste0("sed -e '1, 1d' < /home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_c",i,".BEAGLE.PL > /home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_c",i,".2.BEAGLE.PL \n"))
}
cat("\n")

cat("cat /home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_c1.BEAGLE.PL ") 
for (i in 2:26){
    cat(paste0("/home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_c",i,".2.BEAGLE.PL "))
}
cat(paste0(" > /home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_BEAGLE.PL \n"))
cat("gzip /home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_BEAGLE.PL \n\n")

sink(NULL)
```

### Craete a slurm script file to run pcansgd  
```{r eval=FALSE, message=FALSE, warning=FALSE}

# Run PCAangsd for the entire chromosomes and each chromosome
sink(paste0("../Data/Slurmscripts/runPCAnsgd.sh"))
cat("#!/bin/bash -l\n")
cat(paste0("#SBATCH --job-name=PCAnsgd \n"))
cat(paste0("#SBATCH --mem=16G \n")) 
cat(paste0("#SBATCH --ntasks=8 \n"))
cat(paste0("#SBATCH --nodes=4 \n"))
cat(paste0("#SBATCH -e PCAnsgd.err  \n"))
cat(paste0("#SBATCH --time=72:00:00  \n"))
cat(paste0("#SBATCH -p high  \n"))
cat("\n\n")
cat("module load angsd \n")     
cat("module load deprecated/python \n")     
cat("module load deprecated/pcangsd")     
cat("\n\n")
    
cat("python /home/jamcgirr/apps/pcangsd/pcangsd.py -b /home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_BEAGLE.PL.gz -o /home/ktist/ph/data/angsd/PCAngsd/3pops_MD2000 -threads 16 \n")
sink(NULL)


# run each chromosome separately 
sink(paste0("../Data/Slurmscripts/runPCAnsgd_byChromosome.sh"))
cat("#!/bin/bash -l\n")
cat(paste0("#SBATCH --job-name=PCAnsgdC \n"))
cat(paste0("#SBATCH --mem=16G \n")) 
cat(paste0("#SBATCH --ntasks=8 \n"))
cat(paste0("#SBATCH --nodes=4 \n"))
cat(paste0("#SBATCH -e PCAnsgdbyChrom.err  \n"))
cat(paste0("#SBATCH --time=72:00:00  \n"))
cat(paste0("#SBATCH -p high  \n"))
cat("\n\n")
cat("module load angsd \n")     
cat("module load deprecated/python \n")     
cat("module load deprecated/pcangsd")     
cat("\n\n")

for(i in 1:26){
    cat(paste0("python /home/jamcgirr/apps/pcangsd/pcangsd.py -beagle /home/ktist/ph/data/new_vcf/MD3000/beagle/3pops_MD2000_pruned_c",i,".BEAGLE.PL.gz -o /home/ktist/ph/data/angsd/PCAngsd/3pops_MD2000_c",i," -threads 24 \n"))
}

sink(NULL)

```


# Results of PCAngsd 
## All chromosomes  
```{r eval=FALSE, message=FALSE, warning=FALSE}
pop_info<-read.csv("../Data/Sample_metadata_892pops.csv")
pop_info<-pop_info[,c("Sample","Population.Year","pop","Year.Collected")]
colnames(pop_info)[4]<-"year"
pops<-unique(pop_info$Population.Year[grep("PWS|SS|TB",pop_info$Population.Year)])
pop3<-pop_info[pop_info$Population.Year %in% pops,]

pop3$year[pop3$year==2007|pop3$year==2006]<-"2006/2007"
pop3$year<-factor(pop3$year, levels=c(1991, 1996, "2006/2007", 2017))

C <- as.matrix(read.table(paste0("../Data/PCAangsd/3pops_MD2000.cov")))
e <- eigen(C)
pca <-data.frame(Sample=pop3$Sample, 
                 pop= pop3$pop,
                 year=pop3$year,
                 PC1=e$vectors[,1],PC2=e$vectors[,2],
                 PC3=e$vectors[,3],PC4=e$vectors[,4],
                 PC5=e$vectors[,5],PC6=e$vectors[,6],
                 PC7=e$vectors[,7],PC8=e$vectors[,8],
                 stringsAsFactors=FALSE)

prop_explained <- c()
for (s in e$values[1:10]) {
    #print(s / sum(e$values))
    prop_explained <- c(prop_explained,round(((s / sum(e$values))*100),2))
}
pca$pop<-factor(pca$pop, levels=c("PWS","SS","TB"))

ggplot()+
    geom_point(data = pca, aes(x = PC1, y = PC2, fill = pop, color = pop, shape=year), size = 1.8)+
    scale_fill_manual(values=paste0(cols[c(2,3,1)],"4D"), guide="none")+
    scale_color_manual(values=cols[c(2,3,1)], name="Population")+
    xlab(paste("PC 1: ", prop_explained[1],"%\n",sep = ""))+
    ylab(paste("PC 2: ", prop_explained[2],"%\n",sep = ""))+
    theme_bw()+
    scale_shape_manual(values=c(23,25,3,21), name="Year")
ggsave("../Output/PCA/3pop_allChromosomes.png", height = 3.8, width = 5.2, dpi=300)

```
![](../Output/PCA/3pop_allChromosomes.png)


## Each chromosome separately  
```{r eval=FALSE, message=FALSE, warning=FALSE}

Plots<-list()
chr<-paste0("c", c(1:26))
for (i in 1:length(chr)){
    C <- as.matrix(read.table(paste0("../Data/PCAangsd/3pops_MD2000_",chr[i],".cov")))
    e <- eigen(C)
    pca <-data.frame(Sample=pop3$Sample, 
                     pop=pop3$pop,
                     year=pop3$year,
                     PC1=e$vectors[,1],PC2=e$vectors[,2],
                     PC3=e$vectors[,3],PC4=e$vectors[,4],
                     PC5=e$vectors[,5],PC6=e$vectors[,6],
                     PC7=e$vectors[,7],PC8=e$vectors[,8],
                     stringsAsFactors=FALSE)
    
    prop_explained <- c()
    for (s in e$values[1:10]) {
        prop_explained <- c(prop_explained,round(((s / sum(e$values))*100),2))
    }
    Plots[[i]]<-ggplot()+
                    geom_point(data = pca, aes(x = PC1, y = PC2, fill = pop, color = pop, shape=year), size = 2.5)+
                    scale_fill_manual(values=paste0(cols[c(2,3,1)],"4D"), guide="none")+
                    scale_color_manual(values=cols[c(2,3,1)], name="Population")+
                    xlab(paste("PC 1: ", prop_explained[1],"%\n",sep = ""))+
                    ylab(paste("PC 2: ", prop_explained[2],"%\n",sep = ""))+
                    theme_bw()+
                    scale_shape_manual(values=c(23,25,3,21), name="Year")+
                    ggtitle(paste0("Chr",i))+theme(legend.position = "none")
}
g <- arrangeGrob(do.call(grid.arrange, c(Plots, ncol=5)))
ggsave(g, file="../Output/PCA/3pops_MD2000_PCA_byChromosome.png",width = 35, height = 30)

{pdf("../Output/PCA/3pops_MD2000_PCA_byChromosome.pdf",width = 30, height = 30)
do.call(grid.arrange, c(Plots, ncol=5))
dev.off()
}

# Plot legends
   p<-ggplot()+
        geom_point(data = pca, aes(x = PC1, y = PC2, fill = pop, color = pop, shape=year), size = 2.5)+
        scale_fill_manual(values=paste0(cols[c(2,3,1)],"4D"), guide="none")+
        scale_color_manual(values=cols[c(2,3,1)], name="Population")+
        xlab(paste("PC 1: ", prop_explained[1],"%\n",sep = ""))+
        ylab(paste("PC 2: ", prop_explained[2],"%\n",sep = ""))+
        theme_bw()+
        scale_shape_manual(values=c(23,25,3,21), name="Year")+
        ggtitle(paste0("Chr",i))

as_ggplot(get_legend(p))
ggsave("../Output/PCA/3pops_MD2000_PCA_byChromosome_legend.png",width = 1.5, height = 5)

```

![](../Output/PCA/3pops_MD2000_PCA_byChromosome.png)


## Color by Year  
```{r eval=FALSE, message=FALSE, warning=FALSE}

ycols<-c("#f2f0f7","#cbc9e2","#9e9ac8","#6a51a3")

Plots<-list()
chr<-paste0("c", c(1:26))
for (i in 1:length(chr)){
    C <- as.matrix(read.table(paste0("../Data/PCAangsd/3pops_MD2000_",chr[i],".cov")))
    e <- eigen(C)
    pca <-data.frame(Sample=pop3$Sample, 
                     pop=pop3$pop,
                     year=pop3$year,
                     PC1=e$vectors[,1],PC2=e$vectors[,2],
                     PC3=e$vectors[,3],PC4=e$vectors[,4],
                     PC5=e$vectors[,5],PC6=e$vectors[,6],
                     PC7=e$vectors[,7],PC8=e$vectors[,8],
                     stringsAsFactors=FALSE)
    
    prop_explained <- c()
    for (s in e$values[1:10]) {
        prop_explained <- c(prop_explained,round(((s / sum(e$values))*100),2))
    }
    
    Plots[[i]]<-ggplot()+
                    geom_point(data = pca, aes(x = PC1, y = PC2, fill = year, color = year, shape=pop), size = 2.5)+
                    scale_fill_manual(values=paste0(ycols,"4D"), guide="none")+
                    scale_color_manual(values=ycols, name="Year")+
                    xlab(paste("PC 1: ", prop_explained[1],"%\n",sep = ""))+
                    ylab(paste("PC 2: ", prop_explained[2],"%\n",sep = ""))+
                    theme_bw()+
                    scale_shape_manual(values=c(16,17,15), name="Population")+
                    ggtitle(paste0("Chr",i))+theme(legend.position = "none")
}

{pdf("../Output/PCA/3pops_MD2000_PCA_byChromosome_byYear_nolegends.pdf",width = 30, height = 30)
do.call(grid.arrange, c(Plots, ncol=5))
dev.off()
}

g <- arrangeGrob(do.call(grid.arrange, c(Plots, ncol=4)))
ggsave(g, file="../Output/PCA/3pops_MD2000_PCA_byChromosome_byYear.png",width = 35, height = 40)


# Save the legend
    p<-ggplot()+
            geom_point(data = pca, aes(x = PC1, y = PC2, fill = year, color = year, shape=pop), size = 2.5)+
            scale_fill_manual(values=paste0(ycols,"4D"), guide="none")+
            scale_color_manual(values=ycols, name="Year")+
            xlab(paste("PC 1: ", prop_explained[1],"%\n",sep = ""))+
            ylab(paste("PC 2: ", prop_explained[2],"%\n",sep = ""))+
            theme_bw()+
            scale_shape_manual(values=c(16,17,15), name="Population")+
            ggtitle(paste0("Chr",i))
as_ggplot(get_legend(p))
ggsave("../Output/PCA/3pops_MD2000_PCA_byChromosome_byYear_legend.png",width = 1.5, height = 5)


```

![](../Output/PCA/3pops_MD2000_PCA_byChromosome_byYear.png)


<br>
<br>
<br>

# PCAnsgd Selection scan
## File preps and running PCAngsd  
```{r eval=FALSE, message=FALSE, warning=FALSE}
#Divide the BEAGLE.PL file into each population

# BEAGLE.PL.gz files available upon request
bea<-fread("../Data/vcf/MD2000/3pops_MD2000_pruned_BEAGLE.PL.gz") 
pop_info<-read.csv("../Data/Sample_metadata_892pops.csv")
pop_info<-pop_info[,c("Sample","Population.Year","pop","Year.Collected")]
colnames(pop_info)[4]<-"year"
pops<-unique(pop_info$Population.Year[grep("PWS|SS|TB",pop_info$Population.Year)])


# for each populations
pops3<-c("PWS","TB","SS")
for (i in 1:length(pops3)){
    colums<-grep(pops3[i], colnames(bea))
    vec<-c(1:3, colums)
    df<-bea[,..vec]
    write.table(df, file=gzfile(paste0("../Data/vcf/MD2000/", pops3[i],"_MD2000_pruned.BEAGLE.PL.gz")), sep="\t", quote = F, row.names = FALSE )
}



y1<-c("PWS07","PWS17","PWS91","PWS96")
comb1<-t(combn(y1, 2))
y2<-c("TB06","TB17","TB91","TB96")
comb2<-t(combn(y2, 2))
y3<-c("SS06","SS17","SS96")
comb3<-t(combn(y3, 2))
comb4<-data.frame(V1=c("PWS07","PWS17","PWS91","PWS96","PWS07","PWS17","PWS96","SS06","SS17","SS96"), V2=c("TB06","TB17","TB91","TB96","SS06","SS17","SS96","TB06","TB17","TB96"))
comb<-rbind(comb1,comb2,comb3, comb4)

for (i in 3:nrow(comb)){
    pop1<-comb[i,1]
    pop2<-comb[i,2]
    col1<-grep(pop1, colnames(bea))
    col2<-grep(pop2, colnames(bea))
    vec<-c(1:3, col1, col2)
    df<-bea[,..vec]
    write.table(df, file=gzfile(paste0("../Data/vcf/MD2000/", pop1,"_",pop2,"_MD2000_pruned.BEAGLE.PL.gz")), sep="\t", quote = F, row.names = FALSE )
}


# Create slurm scripts
bfiles<-list.files("../Data/new_vcf/MD2000/", pattern="BEAGLE.PL.gz")

sink("../Data/Slurmscripts/pcansgd_selection_md2000.sh")
cat("#!/bin/bash -l")
cat("\n")
cat(paste0("#SBATCH --job-name=selection \n"))
cat(paste0("#SBATCH --mem=24G \n")) 
cat(paste0("#SBATCH --ntasks=8 \n")) 
cat(paste0("#SBATCH --nodes=4  \n")) 
cat(paste0("#SBATCH -e selection.err  \n"))
cat(paste0("#SBATCH --time=144:00:00  \n"))
#cat(paste0("#SBATCH --mail-user=ktist@ucdavis.edu ##email you when job starts,ends,etc \n"))
#cat(paste0("#SBATCH --mail-type=ALL \n"))
cat(paste0("#SBATCH -p high  \n"))
cat("\n")
cat("module load angsd
module load deprecated/python
module load deprecated/pcangsd")
cat("\n\n")

for (i in 1:length(bfiles)){
  fname<-gsub("_MD2000_pruned.BEAGLE.PL.gz",'', bfiles[i])
  cat(paste0("python /home/jamcgirr/apps/pcangsd/pcangsd.py -beagle /home/ktist/ph/data/new_vcf/MD3000/beagle/",bfiles[i]," -o /home/ktist/ph/data/angsd/selection/",fname,"_selection -selection -sites_save \n"))
}

sink(NULL)

#(ran pcansgd_selection_md2000.sh, pcansgd_selection_md2000_1.sh, pcansgd_selection_md2000_2.sh at Farm on 6/28/23)
```


## Plot the results of each population 
```{r eval=FALSE, message=FALSE, warning=FALSE}
# Selection scan results are available at: OSF Storage: https://osf.io/wrca4 Data/PCAngsd/selection/

pop_info<-read.csv("../Data/Sample_metadata_892pops.csv")
pop_info<-pop_info[,c("Sample","pop","Year.Collected")]
colnames(pop_info)[3]<-"year"


#######
### Selection ###
library(RcppCNPy) # Numpy library for R

## function for QQplot
qqchi<-function(x,...){
    lambda<-round(median(x)/qchisq(0.5,1),2)
    qqplot(qchisq((1:length(x)-0.5)/(length(x)),1),x,ylab="Observed",xlab="Expected",...);abline(0,1,col=2,lwd=2)
    legend("topleft",paste("lambda=",lambda))
}

### read in seleciton statistics (chi2 distributed)
# Each column reflect the selection statistics along a tested PC (they are χ²-distributed with 1 degree of freedom.)
s<-npyLoad("../Data/PCAangsd/selection/PWS_selection.selection.npy")

## make QQ plot to QC the test statistics
qqchi(s)
ncol(s)

## read positions 
p<-read.table("../Data/PCAangsd/selection/PWS_selection.sites",colC=c("factor","integer"),sep=":")
names(p)<-c("chr","pos")


# 1. 1 axis:
#convert test statistic to p-value
p$pval<-1-pchisq(s,1)
p$loc<-1:nrow(p)
p$pval.log<--log10(p$pval)

## make manhatten plot
plot(-log10(p$pval),col=p$chr,xlab="Chromosomes",main="Manhattan plot", pch=".")

# 2. if more than 1 axis (ncol(s)>1)
# p$pval1<-pval[,1]
# p$pval2<-pval[,2]
# p$loc<-1:nrow(p)
# p$pval1.log<--log10(p$pval1)
# p$pval2.log<--log10(p$pval2)

## make Manhattan plots
pops1<-c("PWS","SS","TB")
evens<-paste0("chr",seq(2,26, by=2))

for (i in 1:length(pops1)){
    s<-npyLoad(paste0("../Data/PCAangsd/selection/",pops1[i],"_selection.selection.npy"))
    p<-read.table(paste0("../Data/PCAangsd/selection/",pops1[i],"_selection.sites"),colC=c("factor","integer"),sep=":")
    names(p)<-c("chr","pos")

    n<-ncol(s)
    if (n==1){
        p$pval<-1-pchisq(s,1)
        p$loc<-1:nrow(p)
        p$pval.log<--log10(p$pval)
    }
    #count the number of sites per chromosomes
    poss<-data.frame(chr=paste0("chr",1:26))
    k=1
    for (j in 1:26){
        df<-p[p$chr==paste0("chr",j),]
        poss$start[j]<-k
        poss$end[j]<-k+nrow(df)-1
        k=k+nrow(df)
    }
    poss$x<-poss$start+(poss$end-poss$start)/2

    p$color<-"steelblue"
    p$color[p$chr %in% evens]<-"lightblue"
    ggplot(data=p, aes(x=loc, y=pval.log, color=color))+
        geom_point(size=0.1)+
        scale_color_manual(values=c("lightblue","steelblue"), guide='none')+
        scale_x_continuous(name="Chromosome position", breaks=poss$x, labels=1:26)+
        theme_classic()+ylab("-log10(p-value)")+
        ggtitle(pops1[i])
    ggsave(paste0("../Output/PCA/selection_MD2000/",pops1[i],"_selection_scan.png"), width=8, height=4,dpi=300)
           
}

# -log10(0.05) = 1.30103
# -log10(0.05/232644) #6.667722
###  Significant p-value for 232,644 loci is 6.67  ###



```

![](../Output/PCA/selection_MD2000/PWS_selection_scan.png)
![](../Output/PCA/selection_MD2000/TB_selection_scan.png)

![](../Output/PCA/selection_MD2000/SS_selection_scan.png)

* NO sites above 6.7

## Plot results of pariwise comparison
```{r eval=FALSE, message=FALSE, warning=FALSE}
y1<-c("PWS07","PWS17","PWS91","PWS96")
comb1<-t(combn(y1, 2))
y2<-c("TB06","TB17","TB91","TB96")
comb2<-t(combn(y2, 2))
y3<-c("SS06","SS17","SS96")
comb3<-t(combn(y3, 2))
comb<-rbind(comb1, comb2, comb3)

evens<-paste0("chr",seq(2,26, by=2))

for (i in 1:nrow(comb)){
    pop1<-comb[i,1]
    pop2<-comb[i,2]
    s<-npyLoad(paste0("../Data/PCAangsd/selection/",pop1, "_",pop2,"_selection.selection.npy"))
    p<-read.table(paste0("../Data/PCAangsd/selection/",pop1, "_",pop2,"_selection.sites"),colC=c("factor","integer"),sep=":")
    names(p)<-c("chr","pos")

    n<-ncol(s)
    if (n==1){
        p$pval<-1-pchisq(s,1)
        p$loc<-1:nrow(p)
        p$pval.log<--log10(p$pval)
    }
    if (n>1){
        p$pval<-pval[,1]
        p$loc<-1:nrow(p)
        p$pval.log<--log10(p$pval)
    }
    #count the number of sites per chromosomes
    poss<-data.frame(chr=paste0("chr",1:26))
    k=1
    for (j in 1:26){
        df<-p[p$chr==paste0("chr",j),]
        poss$start[j]<-k
        poss$end[j]<-k+nrow(df)-1
        k=k+nrow(df)
    }
    poss$x<-poss$start+(poss$end-poss$start)/2

    p$color<-"steelblue"
    p$color[p$chr %in% evens]<-"lightblue"
    ggplot(data=p, aes(x=loc, y=pval.log, color=color))+
        geom_point(size=0.1)+
        scale_color_manual(values=c("lightblue","steelblue"), guide='none')+
        scale_x_continuous(name="Chromosome position", breaks=poss$x, labels=1:26)+
        theme_classic()+ylab("-log10(p-value)")+
        ggtitle(paste0(pop1, " - ", pop2))
    ggsave(paste0("../Output/PCA/selection_MD2000/",pop1, "_",pop2,"_selection_scan.png"), width=8, height=4,dpi=300)
}
```
![](../Output/PCA/selection_MD2000/PWS91_PWS96_selection_scan.png)


![](../Output/PCA/selection_MD2000/PWS07_PWS96_selection_scan.png)
![](../Output/PCA/selection_MD2000/PWS07_PWS17_selection_scan.png)
![](../Output/PCA/selection_MD2000/TB91_TB96_selection_scan.png)

![](../Output/PCA/selection_MD2000/TB06_TB96_selection_scan.png)
![](../Output/PCA/selection_MD2000/TB06_TB17_selection_scan.png)

![](../Output/PCA/selection_MD2000/SS06_SS96_selection_scan.png)
![](../Output/PCA/selection_MD2000/SS06_SS17_selection_scan.png)

<br>
<br>
<br>

# NGSadmix

## Run NGSadmix (at Farm or locally)
* NGSadmix_md2000_1.sh,NGSadmix_md2000_2.sh,NGSadmix_md2000_3.sh


## Run CLUMPAL to find best K
* (The best K =3)

### Compile all likelihood numbers from log files into 1 (logfile)

```{bash eval=FALSE}
#linux code (won't work with unix)
(for log in `ls *.log`; do grep -oP '3pops_pruned_maf05_\K[^ ]+|like=\K[^ ]+' $log; done) > 3pops_logfile_k3
(for log in `ls *.log`; do grep -oP '3pps_pruned_maf05_\K[^ ]+|like=\K[^ ]+' $log; done) > 3pops_logfile_k2
(for log in `ls *.log`; do grep -oP '3pops_pruned_maf05_\K[^ ]+|like=\K[^ ]+' $log; done) > 3pops_logfile_k4
```

## Read 'logfile' & create an input file for Clumpak
* http://clumpak.tau.ac.il/bestK.html  
* Upload the log probability table file to the website and submit the form

```{r eval=FALSE, message=FALSE, warning=FALSE}
log2<-read.table("../Data/ngsadmix/3pops_logfile_k2", sep="\t", header =FALSE)
log3<-read.table("../Data/ngsadmix/3pops_logfile_k3", sep="\t", header =FALSE)
log4<-read.table("../Data/ngsadmix/3pops_logfile_k4", sep="\t", header =FALSE)
log4<-data.frame(log4[c(FALSE,TRUE),])

logs<-data.frame(K=c(rep(2, times=10),rep(3, times=10),rep(4, times=10)), Liklihood=c(log2$V1,log3$V1, log4))
write.table(logs, "../Output/ngsadmix/3popslogs.txt", sep="\t", row.names = F, col.names = F, quote=F)
# DO NOT use special character in the file name
# Must have at least three K values

# upload the logs.txt to Clumpak website
# http://clumpak.tau.ac.il

#'Estimating the Best K (from Clumpak)'
#Wed Jul  5 20:15:52 2023: Ln'(3) = 277336.622395895
#Wed Jul  5 20:15:52 2023: Ln'(4) = 264322.030625194
#Wed Jul  5 20:15:52 2023: |Ln''(K=3)| = 13014.5917707011
#Wed Jul  5 20:15:52 2023: Delta(K=3) = 130.085671688741
#Wed Jul  5 20:15:52 2023: Max Delta K: 130.085671688741
#Wed Jul  5 20:15:52 2023: Optimal K by Evanno is: 3
#Wed Jul  5 20:15:56 2023: Using median values of Ln Prob of Data to calculate Prob(K=k):
#Wed Jul  5 20:15:56 2023: Prob(K=2) = 0
#Wed Jul  5 20:15:56 2023: Prob(K=3) = 0
#Wed Jul  5 20:15:56 2023: Prob(K=4) = 1
#Wed Jul  5 20:15:56 2023: Max Probability: 1
#Wed Jul  5 20:15:56 2023: The k for which Prob(K=k) obtains the highest value is: 4
#
#
#
```
![](../Output/ngsadmix/clumpak_results/Best_K_By_Evanno-DeltaKByKGraph.png)

![](../Output/ngsadmix/clumpak_results/Best_K_By_Pritchard-ProbByKGraph.png)

* 2 makes more sense than 3 but the reuslts chose "K=3" as the best


## Run evalAdmix
```{r eval=FALSE, message=FALSE, warning=FALSE}

#Create bash scripts to run locally
sink("../Data/Slurmscripts/evalAdmix_runlocal.sh")
cat("#!/bin/bash\n\n")

for (i in 2:3){
    cat("evalAdmix -beagle Data/new_vcf/MD2000/beagle/3pops_MD2000_pruned_BEAGLE.PL.gz ")
    cat(paste0("-fname Data/ngsadmix/3pops_pruned_maf05_k",i,"_run1.fopt.gz "))
    cat(paste0("-qname Data/ngsadmix/3pops_pruned_maf05_k",i,"_run1.qopt "))
    cat("-P 10 ")
    cat(paste0("-o Data/ngsadmix/evaladmix/output.corres.k",i,".txt\n"))
}
sink(NULL)

```


# Plot results

```{r eval=FALSE, message=FALSE, warning=FALSE}
source("visFuns.R")

#Output files
qfiles<-list.files("../Data/ngsadmix/",pattern="_run1.qopt")
ofiles<-list.files("../Data/ngsadmix/evaladmix/",pattern="output.corres")

#population info
pop<-read.csv("../Data/Sample_metadata_892pops.csv")
pop<-pop[grep("PWS|SS|TB", pop$pop),]
pop$Population.Year<-factor(pop$Population.Year, levels=c("TB91","TB96","TB06","TB17","PWS91","PWS96","PWS07","PWS17","SS96","SS06","SS17"))
poporder<-paste(pop$Population.Year[order(pop$Population.Year)])
pop_order<-c("TB91","TB96","TB06","TB17","PWS91","PWS96","PWS07","PWS17","SS96","SS06","SS17")

for (i in 1:length(qfiles)){
    # extract K from the file name
    if (i!=3) {
        oname<-ofiles[i]
        k<-gsub("[^0-9.]", "", ofiles[i])
        k<-as.integer(gsub("\\.",'',k))
    }
    if (i==3) k=3
    
    #read the qopt file for k=k
    q<-read.table(paste0("../Data/ngsadmix/", qfiles[i]))
    
    #order according to population and plot the NGSadmix results
    q$id<-pop$Population.Year
    q<-q[order(q$id),]
    
    ord<-orderInds(pop = as.vector(poporder), q = q[,1:(i+1)])
    
    xlabels<-data.frame(x=tapply(1:length(poporder),list(poporder), mean))
    xlabels$pop<-factor(rownames(xlabels), levels=pop_order)
    xlabels<-xlabels[order(xlabels$pop),]
    
    #color assignment
    if (i==1) colors=cols[c(1,2)]
    if (i==2) colors=cols[c(1,6,2) ]
    if (i==3) colors=cols[c(1,6,2,3) ]

    {png(paste0("../Output/ngsadmix/3pops_Admix_plot_k",k,".png"), height = 3.5, width=8, unit="in", res=300)
    barplot(t(q[,1:(i+2)])[,ord],col=colors,space=0,border=NA,xaxt="n",xlab="",ylab=paste0("Admixture proportions for K=",k))
    text(xlabels$x,-0.05,xlabels$pop,xpd=T, srt=90, adj=1,cex=0.8)
    abline(v=cumsum(sapply(unique(poporder[ord]),function(x){sum(pop[ord,"Population.Year"]==x)})),col=1,lwd=1.2)
    dev.off()}
    
    #Plot the correlation matrix from evalAdmix
    if (i!=3) {
        r<-read.table(paste0("../Data/ngsadmix/evaladmix/",ofiles[i]))
    
    # Plot correlation of residuals
    {pdf(paste0("../Output/ngsadmix/3pops_evalAdmix_corplot_k",k,".pdf"), height = 8, width=10)
    plotCorRes(cor_mat = r, pop = as.vector(pop[,"Population.Year"]), ord = ord, title=paste0("Evaluation of admixture proportions with K=",k), max_z=0.1, min_z=-0.1)
    dev.off()}
    }
}
```
![](../Output/ngsadmix/3pops_Admix_plot_k2.png)

![](../Output/ngsadmix/3pops_evalAdmix_corplot_k2.png)



![](../Output/ngsadmix/3pops_Admix_plot_k3.png)

![](../Output/ngsadmix/3pops_evalAdmix_corplot_k3.png)




```{r eval=FALSE, message=FALSE, warning=FALSE}
# plot all 10 replicates for k=3
qfiles<-list.files("../Data/ngsadmix/",pattern="^3pops_pruned_maf05_k3+.*.qopt")
for (i in 2:10){
    q<-read.table(paste0("../Data/ngsadmix/", qfiles[i]))
    
    #order according to population and plot the NGSadmix results
    q$id<-pop$Population.Year
    q<-q[order(q$id),]
    
    ord<-orderInds(pop = as.vector(poporder), q = q[,1:3])
    
    xlabels<-data.frame(x=tapply(1:length(poporder),list(poporder), mean))
    xlabels$pop<-factor(rownames(xlabels), levels=pop_order)
    xlabels<-xlabels[order(xlabels$pop),]
    
    #color assignment
    colors=cols[c(1,6,2)]
    
    {png(paste0("../Output/ngsadmix/3pops_Admix_k3_rep",i,".png"), height = 3.5, width=8, unit="in", res=300)
    barplot(t(q[,1:3])[,ord],col=colors,space=0,border=NA,xaxt="n",xlab="",ylab=paste0("Admixture proportions for K=",k), main=paste0("rep",i))
    text(xlabels$x,-0.05,xlabels$pop,xpd=T, srt=90, adj=1,cex=0.8)
    abline(v=cumsum(sapply(unique(poporder[ord]),function(x){sum(pop[ord,"Population.Year"]==x)})),col=1,lwd=1.2)
    dev.off()}
}

```

